Data warehouses have been around for decades, and have established themselves as reliable reporting systems with consistent value. They have also evolved into data marts, specialized appliances and EDW variants to meet emerging needs, but all of these solutions have their drawbacks when it comes to meeting today’s business demands. Some of the common pitfalls to avoid include stale data, slow query performance, long development cycles and high costs associated with these variants. There is a better approach. An operational data warehouse (ODW) addresses the need for operational data analytics with the characteristics listed below, without any of the pitfalls:
As the demands for organizations to operate in real-time or in the moment increase, data warehouses need to deliver ever more current data. SQL Hadoop databases commonly fail to handle continuous streams of updates as the file system is optimized for infrequent batch updates, with a moving-window of historical data. Lack of current data can mean businesses fail to respond to threats and opportunities fast enough to stay competitive.
Built on an underlying architecture optimized for analytic query performance, requiring little or no tuning in anticipation of certain workloads (like indexing or aggregations), maximizing the variety of workloads it can support.
Scales to large data capacities with an economical and flexible storage layer, connecting to a variety of existing legacy and new sources of data.
Offers multiple data protection mechanisms to meet enterprise security requirements and comply with tough regulatory environments.
Offers flexible deployment options, on-premises and multi-cloud options.
Delivers enterprise-level resiliency and manageability.
Such an ODW solution provides a database system that can deliver near-real-time insights for ad hoc self-service data discovery and analytics using the most current operational data storage.